Voice of the DBA

Tag: AI

  • AI Steals Joy

    Take a moment and think about how much work you do each day. If you write code, how much time do you actually spend writing code around meetings, admin work, and other tasks. If you manage systems and develop scripts, how often do you get to work on a script, change it, upgrade it, or add a new feature? I bet it’s a minority of time for most of you. Studies show that many developers spend only an hour or two writing code each day. I would guess it’s lower for sysadmins or DBAs who incorporate coding into their jobs.

    Now think about how much you enjoy each thing you do. I assume meetings aren’t the most exciting thing you work on, nor is updating a document for others to read. When I wrote code as the main part of my job, solving the problem, trying different algorithms, experimenting with enhancements, those were the fun times.

    Now imagine having a competent AI assistant that works with you. What if the AI does the coding and leaves you with the meetings and writing of specifications? It could happen. While it might be fun to direct a team of agents, you still need to understand software architecture and development, but you might not want to get away from writing code.

    As more developers start to use AI agents to get work done, there is rising dissatisfaction among developers because the AI can do quite a bit of the coding, which is the thing that most developers love. The AI doesn’t help with any of the other busy work, though AI agents might do a better job of ensuring docs match committed code (or released code).

    What I’d really like is an AI that would take my place at meetings. Maybe do a report for me or take a summary back, get some action items for me. An AI agent could handle things that wouldn’t necessarily be fun for me. Not all tedious things, but it could free up more time for coding.

    However, that’s not what the GenAI agents are being positioned to do. They’re stealing the joyous part of development. At least, if managers have their way, that’s the place they’ll use GenAI most.

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.

  • T-SQL Tuesday #190–Mastering a New Technical Skill

    It’s time for T-SQL Tuesday again and this time Todd Kleinhans has a great invitation that is near and dear to my heart: mastering a new or existing technical skill. That’s been a lot of what I try to inspire people to do at SQL Server Central.

    Make a plan and start learning. And respond to Todd’s invitation and write down your plan and share it. Start a blog, use Linked In, whatever. Spread the word on socials as well.

    If you want to host, I’m always looking for hosts for T-SQL Tuesday. Ping me on Twitter/X, BlueSky, or LinkedIn.

    Mastering a New Tech Skill

    Like Todd, I’m interested in AI and I think it will dramatically change the world in the coming future. I also think it’s a bit of a technical skill that is important to learn. I wrote about this a bit in last month’s post.

    How do I work with a GenAI model and improve my technical skills? The easy answer is more and more. I’ve been having more conversations with Claude, usually looking for ways to help me solve a problem or write code, and then ask the GenAI to explain things.

    However, I wrote awhile ago about an experiment in helping someone else learn something: Can an AI Help Me Find a Job?. For me, I’ve been looking a bit more at DataBricks, as I hear this from clients all the time. I wanted to gain some skill here, so I decided to ask Claude to help me.

    2025-09_0225

    I got a good outline of things to do across a few months. I then asked for references and got some:

    2025-09_0226

    The next stage for me is to start embarking on this journey a few nights a week and learn some things that might help me both in my job, and potentially in a future position if I need one.

    My Complete Outline from Claude

    Here’s the end result, with links.

    Databricks Learning Outline with Resources

    Phase 1: Foundations (1-2 weeks)

    Understanding the Basics

    • What is Databricks and why it’s used
    • Core concepts: clusters, notebooks, workspaces, and Apache Spark
    • Databricks architecture and modern data stack integration

    Key Resources:

    Phase 2: Getting Started (2-3 weeks)

    Hands-on Basics

    • Navigating workspace interface
    • Creating and managing clusters
    • Working with notebooks
    • Basic data import methods

    Key Resources:

    Phase 3: Data Analysis Fundamentals (3-4 weeks)

    Core Analytics Skills

    • Data exploration and cleaning
    • Working with different data formats
    • Basic SQL and PySpark operations

    Key Resources:

    Phase 4: Intermediate Techniques (4-5 weeks)

    Advanced Analytics

    • Complex transformations and ETL
    • Streaming data and machine learning
    • Performance optimization

    Key Resources:

    Phase 5: Production and Best Practices (2-3 weeks)

    Professional Development

    • Job scheduling and workflows
    • Security and monitoring
    • Integration patterns

    Key Resources:

    Additional Learning Platforms:
    Certification Path:
    Community and Support:
    Getting Started Steps:
    1. Sign up for Databricks Free Edition: Visit the Databricks Free Edition signup page and pick your preferred signup method Databricks Free Edition | Databricks on AWS
    2. Access free training: Free unlimited access to training content on Databricks Academy provides on-demand learning experiences for all skill levels Databricks Launches Free Edition and Announces $100 Million Investment to Develop the Next Generation of Data and AI Talent – Databricks
    3. Join the community: Connect with other learners and experts
    4. Start with sample datasets: Practice with built-in datasets before using your own data

    The great news is that all self-paced training across AI, data engineering, and more is now free for learners Databricks Training & Certification Programs | Databricks, making it easier than ever to get started with Databricks!

  • Learning a New Language

    This week, there are two interesting articles in the newsletter. The first is a markdown getting started article, which helps you learn how to format your documents. At first, I wasn’t sure I liked markdown, but I’ve come to prefer it for a lot of documents. It’s easy to use, and it makes using Git for docs very easy. Plus, I can mix in markdown and HTML as needed, which I really appreciate. I think most people who collaborate and update any documents in a team should learn to use markdown instead of Word, Notepad, etc.

    The second one that caught me eye is one on Power Query. I don’t know Power Query, but there was a piece on the 13 things John Kerski wishes he knew when he started. I didn’t really do more than skim this since I am not writing any Power Query right now. What was interesting to me is that I’m not sure I’d bookmark this if I were actually writing Power Query in Power BI as part of my job.

    Maybe I would, but as I start to use a GenAI more and more to get me started with different projects or requirements, I’m finding it as a way to kickstart me in learning something new. It’s not perfect, and I have to test the results I get. Learning to judge the quality is part of my job, but that’s different than trying to learn something new from scratch. That’s a similar view to what I’ve heard from a few people. This podcast with Kent Beck showcases this view (starts at 6:12).

    In the real world, when I wanted to learn something new, I often looked for a book or a few magazine articles that might get me some basics in a new area. In the digital world, I used to do the same thing, but in the last 20 years, I’ve been looking for an article or a “start in 30 days” book that might give me some basic skills. Then I could experiment and extend what I learned to get work done, likely asking friends or someone in a forum how to do the thing I need.

    Now I ask a GenAI and go from there. I no longer worry about learning the specifics of a language, as I have a good idea in general of the things I need to do. What I’m using the GenAI to do is implement some details, and then I go back and check things. It’s like having an intern who can do a first draft while I work on something else.

    Except it’s way faster.

    GenAI models aren’t perfect, and they make mistakes. However, the more I use them, the more I enjoy them, and I find enough small time savings that I want to use them more.

    Steve Jones

  • The Next Great Thing

    At SQL Bits, I was chatting with Brent about a few things, including AI, which we think is changing the world. I’ve got my set of AI experiments going, and I do believe we will fundamentally alter work and how we use computers in the future.

    Not sure if it’s for the better or worse, but things are changing and will change more.

    Brent mentioned he was thinking about how a few years ago the “next great thing” was blockchain, which was going to change databases and storage, but never really caught on. I never thought it would as it seemed too niche-y for me.

    What other great waves of tech have you seen over the years? I saw a prediction that data engineering will be dead in 5 years. I’ll take that bet. I’ve been hearing the DBA (or other data related positions) will be gone soon for over 25 years. I remember friends asking me if I was worried about my position after SQL Server v7 was released in 1998. I wasn’t then, and I’m not now. It seems there is more work than ever, though certainly the bar is higher for people doing the work.

    You need to learn to be better at your job if you want some job security. That’s one reason I constantly push people to learn, improve, and show off ( consider #SQLNewBlogger posts) their skills. Employers want to know you’re providing value if they’re paying you $$ (or € or £ or ¥ or whatever).

    There have been plenty of tech flops. Virtual/Extended/Augmented reality and various headsets haven’t quite taken off, though there is no shortage of new “AI powered” eyeglass products. Quantum computing still seems to be only slightly less fantastic than fusion energy. Voice control or gestures were supposed to make keyboards obsolete, but we still type a lot.

    There are also some amazing things. Touch computing, especially on mobile, dominates. More and more digital payments are changing our commerce functions. Smartphones are essential devices for most people with apps available for anything and everything.

    I do think GenAI will continue to change the world and how many of us work. This was an interesting talk on how AI changes work, not from a technologist per se, but from a writer. The GenAI models continue to improve, and I find them most useful in saving me minutes, not doing all my work. They are becoming more helpful, though slightly untrustworthy assistants. They are eager teenagers that can help, but need supervision and guidance.

    And sometimes need to be abandoned for the current task and you need to do the rest of (or all of) the work.

    What memories do you have of past “next great things? or maybe of what you see for the future?

    Steve Jones

    Listen to the podcast at Libsyn, Spotify, or iTunes.

    Note, podcasts are only available for a limited time online.